Triple
T38434771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AN/APY-9 |
E903909
|
entity |
| Predicate | associatedAircraftVariant |
P133447
|
FINISHED |
| Object | carrier-capable E-2D variant |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: carrier-capable E-2D variant | Statement: [AN/APY-9, associatedAircraftVariant, carrier-capable E-2D variant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedAircraftVariant Context triple: [AN/APY-9, associatedAircraftVariant, carrier-capable E-2D variant]
-
A.
specificCarrierAircraftVariant
Indicates that one aircraft variant is a specific version designed or adapted for carrier-based operations of another, more general aircraft variant.
-
B.
usesAircraftVariant
Indicates that one entity operates or employs a specific variant or version of an aircraft.
-
C.
relatedAircraft
Indicates that there is an association or connection between two aircraft, such as operational, functional, or contextual relatedness.
-
D.
usedInVariantOfAircraft
chosen
Indicates that something (such as a component, system, or design feature) is utilized in a particular variant of an aircraft.
-
E.
associatedWithAircraftModel
Indicates that something has a specified relationship or connection to a particular aircraft model.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1e32108190897356d6a7fed879 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:31 p.m.